Clustering high dimension, low sample size data using the maximal data piling distance
From MaRDI portal
Publication:5891551
DOI10.5705/ss.2010.148zbMath1238.62073OpenAlexW2325127698MaRDI QIDQ5891551
Young Joo Yoon, Jeongyoun Ahn, Myung Hee Lee
Publication date: 14 May 2012
Published in: Statistica Sinica (Search for Journal in Brave)
Full work available at URL: https://semanticscholar.org/paper/064cf9aacf428e16f3387e4962b4e6271a208040
Lua error in Module:PublicationMSCList at line 37: attempt to index local 'msc_result' (a nil value).
Related Items (7)
Distance-based outlier detection for high dimension, low sample size data ⋮ Some clustering-based exact distribution-free \(k\)-sample tests applicable to high dimension, low sample size data ⋮ Continuum directions for supervised dimension reduction ⋮ Asymptotic properties of hierarchical clustering in high-dimensional settings ⋮ Subspace rotations for high-dimensional outlier detection ⋮ Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings ⋮ Geometric insights into support vector machine behavior using the KKT conditions
This page was built for publication: Clustering high dimension, low sample size data using the maximal data piling distance
